Datasets:

strombergnlp
/

ipm_nel

{"ipm_nel": {"description": "This data is for the task of named entity recognition and linking/disambiguation over tweets. It comprises\nthe addition of an entity URI layer on top of an NER-annotated tweet dataset. The task is to detect entities\nand then provide a correct link to them in DBpedia, thus disambiguating otherwise ambiguous entity surface\nforms; for example, this means linking \"Paris\" to the correct instance of a city named that (e.g. Paris, \nFrance vs. Paris, Texas).\n\nThe data concentrates on ten types of named entities: company, facility, geographic location, movie, musical\nartist, person, product, sports team, TV show, and other.\n\nThe file is tab separated, in CoNLL format, with line breaks between tweets.\nData preserves the tokenisation used in the Ritter datasets.\nPoS labels are not present for all tweets, but where they could be found in the Ritter\ndata, they're given. In cases where a URI could not be agreed, or was not present in\nDBpedia, there is a NIL. See the paper for a full description of the methodology.\n\nFor more details see http://www.derczynski.com/papers/ner_single.pdf or https://www.sciencedirect.com/science/article/abs/pii/S0306457314001034\n", "citation": "@article{derczynski2015analysis,\n title={Analysis of named entity recognition and linking for tweets},\n author={Derczynski, Leon and Maynard, Diana and Rizzo, Giuseppe and Van Erp, Marieke and Gorrell, Genevieve and Troncy, Rapha{\"e}l and Petrak, Johann and Bontcheva, Kalina},\n journal={Information Processing \\& Management},\n volume={51},\n number={2},\n pages={32--49},\n year={2015},\n publisher={Elsevier}\n}\n", "homepage": "https://www.sciencedirect.com/science/article/pii/S0306457314001034", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "uris": {"dtype": "string", "id": null, "_type": "Value"}, "ner_tags": {"feature": {"num_classes": 21, "names": ["O", "B-company", "B-facility", "B-geo-loc", "B-movie", "B-musicartist", "B-other", "B-person", "B-product", "B-sportsteam", "B-tvshow", "I-company", "I-facility", "I-geo-loc", "I-movie", "I-musicartist", "I-other", "I-person", "I-product", "I-sportsteam", "I-tvshow"], "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "ipm_nel2003", "config_name": "ipm_nel", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 96989, "num_examples": 183, "dataset_name": "ipm_nel2003"}}, "download_checksums": {"http://www.derczynski.com/resources/ipm_nel.tar.gz": {"num_bytes": 2409032, "checksum": "c5a2fb618f19b591e6091d1538906db60ae16d2dbe7280533e4c2f8f8dabda9c"}}, "download_size": 2409032, "post_processing_size": null, "dataset_size": 96989, "size_in_bytes": 2506021}}